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Proceedings of the ACM SIGCHI Conference on Human factors in computing systems

作者： Toshifumi Arai Dietmar Aust Scott E. Hudson Hitachi Researeh Laboratory Hitachi Ltd. 1-10mika-cho 7-chome Hitachi Ibaraki 319-12 Japan University of Dortmund Department of Computer Graphics Otto-Hahn Str. 16 44221 Dortmund Germany Georgia Institute of Technology Graphics Visualization and Usability Center and College of Computing Atlanta Georgia

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FluidPlaying: Efficient Adaptive Simulation for Highly Dynamic Fluid

FluidPlaying: Efficient Adaptive Simulation for Highly Dynam...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Sinuo Liu Xiaojuan Ban Sheng Li Haokai Zeng Xiaokun Wang Yanrui Xu Fei Zhu Guoping Wang Graphics & Interaction Lab National Biomedical Imaging Center School of Computer Science Peking University School of Intelligence Science and Technology University of Science and Technology Beijing Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

We present FliudPlaying, a novel dynamic level-based spatially adaptive simulation method that can handle highly dynamic fluid efficiently. To capture the subtle detail of the fluid surface, the high-resolution simulation is performed not only at the free surface but also at those regions with high vorticity levels and velocity difference levels. To minimize the density error, an online optimization scheme is used when increasing the resolution by particle splitting. We also proposed a neighbor-based splash enhancement to compensate for the loss of dynamic details. Compared with the high-resolution simulation baseline, our method can achieve over 3× speedups while consuming only less than 10% computational resources. Furthermore, our method can make up for the loss of high-frequency details caused by the spatial adaptation, and provide more realistic dynamics in particle-based fluid simulation.

关键词： Solid modeling Adaptation models Three-dimensional displays Fluid dynamics Computational modeling Conferences Virtual reality

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Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Mody, Prerak Chaves-De-Plaza, Nicolas F. Rao, Chinmay Astrenidou, Eleftheria de Ridder, Mischa Hoekstra, Nienke Hildebrandt, Klaus Staring, Marius Department of Radiology Leiden University Medical Center Leiden Netherlands HollandPTC consortium Erasmus Medical Center Rotterdam Holland Proton Therapy Centre Delft Leiden University Medical Center Leiden and TU Delft Delft Netherlands Computer Graphics and Visualization Group EEMCS TU Delft Delft Netherlands Department of Radiation Oncology Leiden University Medical Center Leiden Netherlands Department of Radiation Oncology University Medical Center Utrecht Netherlands

Increased usage of automated tools like deep learning in medical image segmentation has alleviated the bottleneck of manual contouring. This has shifted manual labour to quality assessment (QA) of automated contours which involves detecting errors and correcting them. A potential solution to semi-automated QA is to use deep Bayesian uncertainty to recommend potentially erroneous regions, thus reducing time spent on error detection. Previous work has investigated the correspondence between uncertainty and error, however, no work has been done on improving the "utility" of Bayesian uncertainty maps such that it is only present in inaccurate regions and not in the accurate ones. Our work trains the FlipOut model with the Accuracy-vs-Uncertainty (AvU) loss which promotes uncertainty to be present only in inaccurate regions. We apply this method on datasets of two radiotherapy body sites, c.f. head-and-neck CT and prostate MR scans. Uncertainty heatmaps (i.e. predictive entropy) are evaluated against voxel inaccuracies using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves. Numerical results show that when compared to the Bayesian baseline the proposed method successfully suppresses uncertainty for accurate voxels, with similar presence of uncertainty for inaccurate voxels. Code to reproduce experiments is available at https://***/prerakmody/bayesuncertainty-error-correspondence. © 2024, CC BY.

关键词： Image segmentation

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MultiAR: A Multi-User Augmented Reality Platform for Biomedical Education

MultiAR: A Multi-User Augmented Reality Platform for Biomedi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Markus Perz Gijs Luijten Jens Kleesiek Dieter Schmalstieg Jan Egger Christina Gsaxner Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Institute for Artificial Intelligence in Medicine University Medicine Essen Essen Germany Cancer Research Center Cologne Essen West German Cancer Center Essen Germany Institute for Visualization and Interactive Systems University of Stuttgart Stuttgart Germany Virtual and Extended Reality in Medicine (ZvRM) University Hospital Essen Essen Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper addresses the growing integration of Augmented Reality (AR) in biomedical sciences, emphasizing collaborative learning experiences. We present MultiAR, a versatile, domain-specific platform enabling multi-user interactions in AR for biomedical education. Unlike platform-specific solutions, MultiAR supports various AR devices, including handheld and head-mounted options. The framework extends across domains, augmenting biomedical education applications with collaborative capabilities. We define essential requirements for a multi-user AR framework in education, detail MultiAR’s design and implementation, and comprehensively evaluate it using anatomy education examples. Quantitative and qualitative analyses, covering system performance, accuracy metrics, and a user study with 20 participants, highlight the urgent need for a tailored collaborative AR platform in biomedical education. Results underscore enthusiasm for collaborative AR technology, endorsing MultiAR as an accessible, versatile solution for developers and end-users in biomedical education.

关键词： Measurement Accuracy Federated learning System performance Education Collaboration Anatomy Engineering in medicine and biology Augmented reality

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Remote computing environment compensating for brain shift

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computer Aided Surgery 2003年第4期8卷 169-179页

作者： Hastreiter, Peter Engel, Klaus Soza, Grzegorz Wolf, Matthias Ganslandt, Oliver Fahlbusch, Rudolf Greiner, Gnther Ximsky, Christopher Department of Neurosurgery Neurocenter Friedrich Alexander University of Erlangen-Nuremberg D-91054 Erlangen Schwabachanlage 6. Germany Computer Graphics Group University of Erlangen-Nuremberg Erlangen Germany Visualization and Interactive Systems Group University of Stuttgart Erlangen Germany Knowledge Processing Group Bavarian Research Center for Knowledge-Based Systems Erlangen Germany

Objective: Anatomical and functional image data become invalid during an operation due to brain shift. Compensation is achieved by using intraoperative imaging to update anatomical information. To accelerate the registration and visualization of pre- and intraoperative image data, the presented work focuses on remote computing capabilities. The underlying framework efficiently combines local desktop computers and remote high-end graphics workstations exploiting expensive hardware. Methods: By performing all computations on the remote computer, the MR volumes are rigidly aligned via voxel-based registration. Using graphics hardware for acceleration, all interpolation operations are performed with 3D texture-mapping hardware. A new approach then transforms functional markers from preoperative measurements to the intraoperative situation using an automatic tracking algorithm to identify corresponding sulci. Communicating Java viewers are suggested for analyzing the results interactively on a local computer, with all calculations being performed exclusively on the remote computer. Results: The suggested approach was successfully applied in S cases using MR data containing functional markers of MEG and fMRI measurements identifying eloquent brain areas. Remote large-scale graphics hardware was thereby efficiently made available for fast registration and interactive direct volume rendering in neurosurgery. Conclusion: Overall, the presented framework demonstrates efficient access of expensive high- end hardware remotely controlled by thin clients, and further emphasizes the need to compensate for brain shift in functional neuronavigation. © 2003 Informa UK Ltd All rights reserved.

关键词： 3D visualization brain shift registration remote computing

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Impact of Implementing Game Elements in Gamifying Educational Environment: A Study

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Journal of Physics: Conference Series 2019年第1期1358卷

作者： F Durin R Lee A Bade C K On N Hamzah Mathematics Graphics and Visualization Research Group (M-GRAVS) Faculty of Science and Natural Resources Universiti Malaysia Sabah 88450 Kota Kinabalu Sabah Malaysia Mathematics and Computer Graphics Faculty of Science and Natural Resources Universiti Malaysia Sabah Center of Excellence in Semantic Agents Universiti Malaysia Sabah Sabah Malaysia

Gamification is the use of game elements in a non-gaming environments. The aims of implementing game elements in gamifying educational environment is to make the learners engaged and motivated in the learning process. But, some of the research result giving bad impacts in implementing gamification in education. The objective of this research is to study the impact of implementing game elements in education. The aim of this research is to study which game elements is most commonly used in gamifying educational environment. The study is done by reviewing previous research on impacts of implementing game element in education from 2008 until 2018 to see whether the elements giving positive or negative impacts on education. The results shows the most common game elements used in gamification is rewards, feedback, challenge, quest/mission/goal, level/stage, point/score, avatars/players, task, character, time-limit, narrative/dialogue, leaderboards, progress bars, and badges. Majority of the research on gamification in education reports positive impacts by introducing gamification in education. In conclusion, by implementing game elements in the gamifying educational environment is a good method to create a good adjustment in learners' behavior and assertiveness in the learning process, it can develop engagement and motivation of the learners'.

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IDEAS: Immersive dome experiences for accelerating science

arXiv

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arXiv 2019年

作者： Faherty, Jacqueline K. SubbaRao, Mark Wyatt, Ryan Ynnerman, Anders deGrasse Tyson, Neil Geller, Aaron Weber, Maria Rosenfield, Philip Stoeckle, Gabriel Weiskopf, Daniel Magnor, Marcus Williams, Peter K.G. Abbott, Brian Marchetti, Lucia Jarrrett, Thomas Fay, Jonathan Peek, Joshua Graur, Or Durrell, Patrick Homeier, Derek Preston, Heather Müller, Thomas Vos, Johanna M. Brown, David Godfrey, Paige Giorla Rice, Emily Gagliuffi, Daniella Bardalez Bock, Alexander American Museum of Natural History Hayden Planetarium Adler Planetarium International Planetarium Society California Academy of Sciences Linköping University Adler Planetarium University of Chicago AAS WorldWide Telescope Instituto de Astronomiá UNAM Natural History Museum Vienna Austria Visualization Research Center University of Stuttgart Computer Graphics Lab TU Braunschweig Center for Astrophysics | Harvard & Smithsonian American Astronomical Society American Museum of Natural History Hayden Planetarium University of Cape Town Iziko Planetarium & Digital Dome Space Telescope Science Institute Johns Hopkins University Youngstown State University Ward Beecher Planetarium Förderkreis Planetarium Göttingen Germany Calusa Nature Center & Planetarium Haus der Astronomie MPIA Microsoft Research Slooh CUNY Macaulay Honors College Scientific Computing and Imaging Institute University of Utah

Astrophysics lies at the crossroads of big datasets (such as the Large Synoptic Survey Telescope and Gaia), open source software to visualize and interpret high dimensional datasets (such as Glue, WorldWide Telescope, and OpenSpace), and uniquely skilled software engineers who bridge data science and research fields. At the same time, more than 4,000 planetariums across the globe immerse millions of visitors in scientific data. We have identified the potential for critical synergy across data, software, hardware, locations, and content that-if prioritized over the next decade-will drive discovery in astronomical research. Planetariums can and should be used for the advancement of scientific research. Current facilities such as the Hayden Planetarium in New York City, Adler Planetarium in Chicago, Morrison Planetarium in San Francisco, the Iziko Planetarium & Digital Dome Research Consortium in Cape Town, and visualization center C in Norrköping are already developing software which ingests catalogs of astronomical and multi-disciplinary data critical for exploration research primarily for the purpose of creating scientific storylines for the general public. We propose a transformative model whereby scientists become the audience and explorers in planetariums, utilizing software for their own investigative purposes. In this manner, research benefits from the authentic and unique experience of data immersion contained in an environment bathed in context and equipped for collaboration. Consequently, in this white paper we argue that over the next decade the research astronomy community should partner with planetariums to create visualization-based research opportunities for the field. Realizing this vision will require new investments in software and human capital. Copyright © 2019, The Authors. All rights reserved.

关键词： Planetariums

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PO-260 Deep phenotyping of colorectal cancers by high-dimensional mass cytometry reveals tumor-specific immune landscapes

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ESMO Open 2018年 3卷 A328-A329页

作者： Vries, N. De Unen, V. Van Abdelaal, T. Herk, M. Van Van der Breggen, R. Sarasqueta, A. Farina Höllt, T. Lelieveldt, B. Koning, F. Miranda, N. De Leiden University Medical Center Immunohematology and Blood Transfusion Leiden The Netherlands Delft University of Technology Pattern Recognition and Bioinformatics Group Delft The Netherlands Leiden University Medical Center Pathology Leiden The Netherlands Delft University of Technology Computer Graphics and Visualization Group Delft The Netherlands Leiden University Medical Center Computational Biology Center Leiden The Netherlands Leiden University Medical Center Radiology Leiden The Netherlands

ABSTRACTIntroductionImmune checkpoint blockade has revolutionised cancer treatment. However, clinical outcomes are highly variable as only a proportion of cancer patients benefit from this treatment. To understand the mechanisms that determine responses to current immunotherapies and for the design of alternative approaches, it is crucial to characterise the immune landscape of the tumour microenvironment. An in-depth understanding of anti-tumour immunity requires a comprehensive analysis of the immune cell populations that participate in the process of tumorigenesis. The aim of this study is to unravel local and systemic profiles of lymphoid and myeloid immune subsets in colorectal cancer (CRC) using high-dimensional immunophenotyping by mass cytometry. Material and methodsThe expression of 36 immune cell markers was simultaneously assessed at the single-cell level by mass cytometry in tumour tissues (n=19), tumor-associated lymph nodes (n=17), adjacent normal mucosa (n=4), and peripheral blood samples (n=9) derived from 18 CRC patients. In total, 9 million cells were included in the analysis. Cytosplore and HSNE (Hierarchical Stochastic Neighbour Embedding) analysis tools were used to identify and visualise the composition of the lymphoid and myeloid cell populations of the innate and adaptive immune compartments in the tissues. Results and discussionsWe identified 220 immune cell subsets, including tumor-resident CD8 +CD45RO +CD103 +PD-1 +, CD4 +CD45RO +CD103 +PD-1 + and CD4 +CD45RO +ICOS +CD27 - T cell populations that were not found in tumor-associated lymph nodes, adjacent normal mucosa, and peripheral blood samples of CRC patients. In addition, we identified an overrepresentation of CD3 -CD7 +CD56 +CD45RO +CD127 - intermediate innate lymphoid cells in mismatch-repair deficient cancers and an enrichment of CD14 - myeloid populations in mismatch-repair proficient cancers. Unsupervised clustering of the samples based on the composite immune profile separated the

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62. The virtual surgical pelvis: A highly-detailed 3D pelvic model for anatomical education and surgical simulation

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European Journal of Surgical Oncology 2014年第11期40卷 S32-S32页

作者： Kraima, A. Smit, N.N. Jansma, D. West, N.P. Quirke, P. Rutten, H.J. Vilanova, A. Van de Velde, C.J.H. DeRuiter, M.C. Leiden University Medical Center Leiden Netherlands Delft University of Technology Computer Graphics and Visualization Delft Netherlands Leiden University Medical Center Anatomy & Embryology Leiden Netherlands Leeds Institute of Cancer and Pathology Pathology Anatomy and Tumour Biology Leeds United Kingdom Catherina Hospital Eindhoven Surgery Eindhoven Netherlands Leiden University Medical Center Surgery Leiden Netherlands

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Highlights from the 5th Symposium on Biological Data visualization: Part 2

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BMC proceedings 2015年第SUPPL 6 PROCEEDINGS OF THE 5TH SYMPOSIUM ON BIOLOGICAL DATA期9卷 S1页

作者： Jan Aerts G Elisabeta Marai Kay Nieselt Cydney Nielsen Marc Streit Daniel Weiskopf Visual Data Analysis Lab ESAT/STADIUS KU Leuven Kasteelpark Arenberg 10 3001 Leuven Belgium iMinds Medical IT KU Leuven Kasteelpark Arenberg 10 3001 Leuven Belgium. Electronic Visualization Lab University of Illinois at Chicago 851 S. Morgan St Chicago IL 60607 USA. Center for Bioinformatics University of Tübingen Sand 14 72076 Tübingen Germany. Department of Pathology and Laboratory Medicine University of British Columbia and British Columbia Cancer Agency 675 West 10th Avenue Vancouver British Columbia V5Z 1L3 Canada. Institute of Computer Graphics Johannes Kepler University Linz Altenberger Straße 69 4020 Linz Austria. VISUS University of Stuttgart Allmandring 19 70569 Stuttgart Germany.

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